Question 502 of 753
AI Associate Ethical Considerations of AI Practice Question
Exhibit
Refer to the exhibit. ``` Model: Churn Predictor v2 Training Data: 80% male, 20% female Accuracy: 85% overall, 90% male, 60% female Fairness Metric: Equal Opportunity Difference = 0.3 ```
Refer to the exhibit. What is the most likely cause of the fairness issue?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The training data is imbalanced, causing the model to perform better on the majority group.
Imbalanced training data often leads to disparate performance. Option A is wrong because the model is not inherently biased. Option C is wrong because overall accuracy can be high despite bias. Option D is wrong because there is no indication of overfitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model overfits to the male group.
Why it's wrong here
Overfitting is not indicated by lower accuracy on minority.
- ✓
The training data is imbalanced, causing the model to perform better on the majority group.
Why this is correct
Imbalanced data leads to unequal performance.
- ✗
The overall accuracy is too low.
Why it's wrong here
Overall accuracy is 85%, which is acceptable.
- ✗
The model is inherently biased against females.
Why it's wrong here
Bias stems from data, not model itself.
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Last reviewed: Jun 22, 2026
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